O ensino da psicologia no curso de administração: como trabalhar em pequenos grupos favorecendo a relação de equipes

A autora, em seu trabalho na área de consultoria, observou que as pessoas não são instrumentalizadas em sua formação para operar nas interações. Ainda, que haja uma dificuldade muito grande em aplicar as informações, obtidas através de treinamentos a que são submetidos, às situações cotidianas do trabalho. Os processos grupais não são percebidos na sua amplitude, na medida em que são enfatizados os resultados, sem que o percurso para neles se chegar seja reconhecido vivencialmente. Neste sentido, muitos cursos são oferecidos, mas nem todas as pessoas passam a se dar conta de resistências, obstáculos inerentes a toda e qualquer interação. Ainda existe uma percepção forte de que o simples conhecimento evita distorções, e assim os processos internos se são conhecidos, são pouco vivenciados, e portanto há pouca possibilidade de transformá-los, quando não podem ser identificados, nomeados e aí então articulados com o teórico. Há uma cisão entre a teoria e a vivência. Foi refletindo sobre essas questões que esse trabalho buscou criar uma metodologia que auxiliasse o conteúdo da Psicologia e o trabalho em equipe.

A psicologia no curso de administração: reflexão sobre a produção em pequenos grupos e a confecção de textos pelos alunos

Este trabalho ocorreu em uma classe de 52 alunos, na disciplina de Psicologia, no Curso de Administração. Centrou sua análise na confecção de textos pelos alunos articulando-os à experiência do seu cotidiano, em pequenos grupos, a partir da devolutiva do processo grupal de aprendizagem como instrumental que possibilitou a maior discriminação entre conteúdos próprios dos integrantes e do grupo.Investigou também como a identificação de atitudes defensivas, no grupo e em si, contribuiu para a elevação do nível de tolerância às contradições, possibilitando uma visão questionadora da dinâmica visível e invisível, presentes em sua produção. Seu objetivo esteve ligado ao desenvolvimento de uma atitude de contenção e apoio necessária ao papel do futuro administrador.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).